[MATH-177] Provide a general minimizing package with a classical Gauss-Newton algorithm

• Type: Improvement
• Status: Closed
• Priority: Major
• Resolution: Fixed
• Affects Version/s: 2.0
• Fix Version/s: 2.0
• Assignee: Luc Maisonobe
• Reporter: Mick
• Created: 16/Jan/08 21:02
• Updated: 23/Apr/09 02:26
• Resolved: 27/Mar/09 07:59

Description

Currently the math API provides least squares only method for minimizing (solving). The limitation to least-squares problems comes from the Levenberg-Marquardt algorithm. A more general minimizer (not for quadratic forms) could be implemented by refactoring this with a classical GN, steepest descent and also conjugate gradient. We could use them as a basis for some least-squares solvers (and also keep the very efficient and specialized Levenberg-Marquardt too).

Based on email exchange with Luc Maisonobe entitled [math] Minimizer on 1/15/08.

Issue Links

• is related to: MATH-246 Simplex Method Implementation